Bibliographic record
Abstract
This is the first of three prospective papers examining how well forecasters can predict the future time path of short-term interest rates. Most prior work has been done using US data; in this exercise we use forecasts made for New Zealand (NZ) by the Reserve Bank of New Zealand (RBNZ), and those derived from money market yield curves in the UK. In this first exercise we broadly replicate recent US findings for NZ and UK, to show that such forecasts in NZ and UK have been excellent for the immediate forthcoming quarter, reasonable for the next quarter and useless thereafter. Moreover, when ex post errors are assessed depending on whether interest rates have been upwards, or downwards, trending, they are shown to have been biased and, apparently, inefficient. In the second paper we shall examine whether (NZ and UK) forecasts for inflation exhibit the same syndromes, and whether errors in inflation forecasts can help to explain errors in interest rate forecasts. In the third paper we shall set out an hypothesis to explain those findings, and examine whether the apparent ex post forecast inefficiencies may still be consistent with ex ante forecast efficiency. Even if the forecasts may be ex ante efficient, their negligible ex post forecasting ability suggests that, beyond a six months’ horizon from the forecast date, they would be better replaced by a simple ‘no-change thereafter’ assumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".